This paper presents a case study in system identification for limit
cycling systems. The focus of the paper is on (a) the use of model
structure derived from physcal considerations and (b) the use of algorithms
for the identification of component subsystems of this model structure.
The physical process used in this case study is that of a reduced order
model for combustion instabilities for lean premixed systems. The
identification techniques applied in this paper are the use of linear system
identification tools (prediction error methods), time delay estimation (based on
Kalman filter harmonic estimation methods) and qualitative validation of
model properties using harmonic balance and describing function methods.
The novelty of the paper, apart from its practical application, is that
closed loop limit cycle data is used together with a priori process
structural knowledge to identify both linear dynamic forward and nonlinear
feedback paths. Future work will address the refinement of the process
presented in this paper, the use of alternative algorithms and also the use
of control approachs for the validated model structure obtained from
this paper